During: OF vs OC

library(tidyverse)
library(biomaRt)
library(ggrepel)
library(clusterProfiler)
library(enrichplot)
# Parallel
library(BiocParallel)
register(MulticoreParam(6))

load('../data/microarray_NGS_objects.Rdata')
load('../data/top_tables.Rdata')
sva_counts <- read_tsv('../data/sva_counts.tsv.gz')

Rows: 14318 Columns: 66
── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: "\t"
chr  (1): Gene
dbl (65): GSM2944692, GSM2944693, GSM2944694, GSM2944695, GSM2944696, GSM2944697, GSM2944698, GSM2944699, GSM2944700, GSM327854, GSM327855, GSM327856, GSM3278...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

sample_meta_D <- sample_meta %>% filter(Sample %in% colnames(sva_counts)) %>%
  dplyr::select(Sample:Section, Layout:Fusion) %>%
  mutate(S2 = case_when(Section == 'OF' ~ 'OF', TRUE ~ 'OC')) %>%
  unique()

box_maker <- function(table, genes, section = c('OF','OC'), type = 'temporal'){
  if ('matrix' %in% class(table)){
    table <- table %>%
      as_tibble(rownames = 'Gene')
  }
  if (type == 'temporal'){
    plot <- table %>% 
      pivot_longer(-Gene, names_to = 'Sample', values_to = 'Expression') %>%
      mutate(Sample = gsub('_.*|.CEL.*','',Sample)) %>%
      left_join(sample_meta_D) %>%
      mutate(S2 = case_when(Section == 'OF' ~ 'OF',TRUE ~ 'OC')) %>%
      filter(Gene %in% genes, S2 %in% section) %>%
      #filter(Gene %in% row.names(top.table_OF_AD %>% head(10))) %>%
      mutate(Fusion = factor(Fusion, levels = c('Before','During','After'))) %>%
      mutate(OrgTech = paste(Organism, Technology, sep = '_')) %>% 
      ggplot(aes(x=Fusion, y=Expression, color = Organism, shape = Technology)) +
      # geom_boxplot(aes(group = Fusion), color = 'Black', outlier.colour = NA) +
      # geom_boxplot(aes(group = interaction(Organism,Fusion)), outlier.colour = NA) +
      ggbeeswarm::geom_quasirandom(size = 3, alpha = 0.7) +
      cowplot::theme_cowplot() +
      facet_grid(~Gene + S2, scales = 'free_y') +
      ggsci::scale_color_aaas() +
      ylab('log2 (corrected counts)') +
      stat_summary(fun=mean, geom = 'line', aes(group = OrgTech, color = Organism)) }
  else {
    plot <- table %>% 
      pivot_longer(-Gene, names_to = 'Sample', values_to = 'Expression') %>%
      mutate(Sample = gsub('_.*|.CEL.*','',Sample)) %>%
      left_join(sample_meta_D) %>%
      mutate(S2 = case_when(Section == 'OF' ~ 'OF',TRUE ~ 'OC')) %>%
      filter(Gene %in% genes, S2 %in% section) %>%
      mutate(Fusion = factor(Fusion, levels = c('Before','During','After'))) %>%
      filter(Fusion == 'During') %>% 
      mutate(OrgTech = paste(Organism, Technology, sep = '_')) %>% 
      ggplot(aes(x=S2, y=Expression, color = Organism, shape = Technology)) +
      # geom_boxplot(aes(group = Fusion), color = 'Black', outlier.colour = NA) +
      # geom_boxplot(aes(group = interaction(Organism,Fusion)), outlier.colour = NA) +
      ggbeeswarm::geom_quasirandom(size = 3, alpha = 0.7) +
      cowplot::theme_cowplot() +
      ggsci::scale_color_aaas() +
      ylab('log2 (corrected counts)') +
      xlab('Section') +
      stat_summary(fun=mean, geom = 'line', aes(group = OrgTech, color = Organism)) + facet_wrap(~Gene)
  }
  plot
}

volcano_maker <- function(df, 
                          title="Volcano Plot", 
                          pvalue='P.Value', 
                          padj='adj.P.Val', 
                          logFC='logFC', 
                          gene_list = ''){
  df$pvalue <- df[,pvalue]
  df$log2FoldChange <- df[,logFC]
  df$padj <- df[,padj]
  df$Gene <- row.names(df)
  df <- df[!is.na(df$pvalue),]
  print(dim(df))
  
  df <- df %>% mutate(Class = case_when(padj < 0.05 & abs(logFC) > 1~ "FDR < 0.05 & logFC > 1",
                                        padj < 0.1 & abs(logFC) > 1 ~ 'FDR < 0.1 & logFC > 1',
                                        TRUE ~ 'Not significant'))
  df$GeneT <- df$Gene
  if (gene_list == ''){
    gene_list <- df %>% filter(padj < 0.05) %>% pull(Gene) %>% head(10)
  }
  df$Gene[!df$Gene %in% gene_list] <- ''
  
  plot <- ggplot(data=df,aes(label=Gene, x = log2FoldChange, y = -log10(pvalue))) +
    geom_point(aes(colour=Class)) +
    scale_colour_manual(values=c("darkred", "red", "grey")) +
    cowplot::theme_cowplot() +
    geom_vline(aes(xintercept=-1),linetype="dotted") +
    geom_vline(aes(xintercept=1),linetype="dotted") +
    geom_vline(aes(xintercept=-2),linetype="dotted") +
    geom_vline(aes(xintercept=2),linetype="dotted") +
    geom_label_repel(max.overlaps = 100) +
    xlab('logFC') + ylab('-log10(p value)') +
    ggtitle(title) + cowplot::theme_cowplot()
  
  plot
}

During vs Before (OF)

2021-12-06

Positive means higher expression in the OF relative to the OCn This test is designed to identify which genes are changing between the OF and OC in the “during” stage.

Volcano

volcano_maker(top.table_During, title = 'During: OF vs OC',
              
              gene_list = c(top.table_During %>% filter(logFC > 0) %>% head(12) %>% row.names(),
                            top.table_During %>% filter(logFC < 0) %>% head(12) %>% row.names()))

[1] 14318    10

Warning in if (gene_list == "") { :
  the condition has length > 1 and only the first element will be used

Diff Table

All genes with an FDR < 0.2. Not very many. There are a limited number of samples before the fusion begins (for the OF - no human) compared to the other time points.

top.table_During %>% as_tibble(rownames = 'Gene') %>% filter(adj.P.Val < 0.1) %>%  DT::datatable()

Expression of top 6 genes (by FDR) across section and stage

Colored by organism. Each line is drawn for organism / technology (remember, mouse has both microarray and RNA-seq).

box_maker(sva_counts, 
          genes = top.table_During %>% 
            as_tibble(rownames = 'Gene') %>% 
            filter(adj.P.Val < 0.05) %>% head(24) %>% pull(Gene),
          section = c('OF','OC'), type = 'bloop')

Joining, by = "Sample"

Enrichment Analysis (GO, GSEA) Before -> During

GSEA

GSEA uses a ranked list of genes by logFC. So the p values are not used in this situation. The order is. So the GSEA is useful in situations where there are very few differentially expressed genes.

Positive terms (higher in OF) relate to collagen / extracellular matrix, basement membrane. Negative terms (higher in OC) relate to cell cycle.

Dotplot

all_genes <- bitr(top.table_During %>% as_tibble(rownames = 'Gene') %>% pull(Gene), fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")

'select()' returned 1:many mapping between keys and columns
Warning in bitr(top.table_During %>% as_tibble(rownames = "Gene") %>% pull(Gene),  :
  0.01% of input gene IDs are fail to map...

all_genes <- all_genes %>% left_join(top.table_During %>% as_tibble(rownames = 'SYMBOL'), by = c('SYMBOL'))

logFC <- all_genes$logFC
names(logFC) <- all_genes$ENTREZID
logFC <- na.omit(logFC)

logFC = sort(logFC, decreasing = TRUE)

gse <- gseGO(geneList=logFC,
             ont ="ALL",
             keyType = "ENTREZID",
             pvalueCutoff = 0.05,
             OrgDb = org.Hs.eg.db,
             pAdjustMethod = "BH",
             eps = 0)

preparing geneSet collections...
GSEA analysis...
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam,  :
  There are ties in the preranked stats (0.09% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
leading edge analysis...
done...

gse <- setReadable(gse, OrgDb = org.Hs.eg.db)
# change sort logic
gseF <- gse
gseF@result <- rbind(gseF@result %>% arrange(NES) %>% head(20),
                     gseF@result %>% arrange(NES) %>% tail(20) %>% arrange(-NES)
)
dotplot(gse, showCategory=15, split=".sign") + facet_grid(.~.sign) + cowplot::theme_cowplot()

wrong orderBy parameter; set to default `orderBy = "x"`

Related terms

So we can see “chunks” of terms that go together. We see several terms related to the structural components of the cell. As well as a set of cell cycle terms.

gsePT <- pairwise_termsim(gse)
emapplot(gsePT)

Table

So you can see the genes in the ontology term. The genes get “included” as enriched if GSEA deems them to be ranked unusually high.

gse@result %>% as_tibble() %>% arrange(-abs(NES)) %>% filter(p.adjust < 0.05) %>%  DT::datatable()

GO Enrichment

GO enrichment uses a cutoff between differentially expressed genes (FDR < 0.1 in this case) and everything else.

Loads of stuff relating to visual function and development.

diff_genes <- top.table_During %>% as_tibble(rownames = 'Gene') %>% filter(adj.P.Val < 0.1) %>% 
  mutate(Group = case_when(logFC > 0 ~  'Higher in OF', TRUE ~ 'Higher in OC'))
eg_diff_genes <- bitr(diff_genes$Gene, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")

'select()' returned 1:1 mapping between keys and columns

eg_diff_genes <- diff_genes %>% left_join(., eg_diff_genes, by = c('Gene' = 'SYMBOL'))
eg_universe = bitr(top.table_During %>% as_tibble(rownames = 'Gene') %>% pull(Gene), fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")

'select()' returned 1:many mapping between keys and columns
Warning in bitr(top.table_During %>% as_tibble(rownames = "Gene") %>% pull(Gene),  :
  0.01% of input gene IDs are fail to map...

eg_diff_gene_list <- eg_diff_genes$logFC
names(eg_diff_gene_list) <- eg_diff_genes$ENTREZID

egoOF_OC <- enrichGO(gene          = eg_diff_genes$ENTREZID,
                     universe      = eg_universe$ENTREZID,
                     OrgDb         = org.Hs.eg.db,
                     ont           = "all",
                     readable      = TRUE)



p1 <- dotplot(egoOF_OC, showCategory=20) + ggtitle("Dotplot for GO, OF vs OC")

wrong orderBy parameter; set to default `orderBy = "x"`

p1

NA
NA

Table

So you can see the genes in the ontology term.

egoOF_OC@result %>% as_tibble() %>%  filter(p.adjust < 0.05) %>%  DT::datatable()

CNET Plot

Relationships between related GO terms with shared genes. Yellow means more expressed in the OF than the OC.

geneList <- eg_diff_genes$logFC
names(geneList) <- eg_diff_genes$Gene
cnet <- cnetplot(egoOF_DB, foldChange = geneList, showCategory = 12) + scale_color_viridis_c(name = 'log2(FoldChange)')

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

cnet

Wikipathways

# system("wget https://wikipathways-data.wmcloud.org/current/gmt/wikipathways-20211110-gmt-Homo_sapiens.gmt")
wp2gene <- read.gmt('wikipathways-20211110-gmt-Homo_sapiens.gmt')
wp2gene <- wp2gene %>% tidyr::separate(term, c("name","version","wpid","org"), "%")
wpid2gene <- wp2gene %>% dplyr::select(wpid, gene) #TERM2GENE
wpid2name <- wp2gene %>% dplyr::select(wpid, name) #TERM2NAME

ewp <- enricher(eg_diff_genes$ENTREZID,
                TERM2GENE = wpid2gene,
                TERM2NAME = wpid2name,
                pvalueCutoff = 0.1)

ewp_plot <- dotplot(ewp, showCategory=10) + ggtitle("Dotplot for WikiPathways")

wrong orderBy parameter; set to default `orderBy = "x"`

ewp_plot

Table

ewp <- setReadable(ewp, OrgDb =  org.Hs.eg.db, keyType = 'ENTREZID')
ewp@result %>% DT::datatable()

KEGG Pathway Enrichment

kk <- enrichKEGG(gene         = eg_diff_genes$ENTREZID, 
                 universe = eg_universe$ENTREZID,
                 organism     = 'hsa')

Reading KEGG annotation online:

Reading KEGG annotation online:

dotplot(kk) + ggtitle("KEGG Pathway Enrichment") 

wrong orderBy parameter; set to default `orderBy = "x"`

Table

kk <- setReadable(kk, OrgDb =  org.Hs.eg.db, keyType = 'ENTREZID')
kk@result %>% DT::datatable()

My takeaway(s)

1. Many many genes are different.
2. Many relate to retina development
3. In the WikiPathways, the set relating to Neural Crest differentiation is intersting. You see MITF/DCT (melanosomes). MSX2 is a TF that is involved in craniofacial morphogenesis and limb pattern formation. This TF is downregulated in the fissure.

Session Info

devtools::session_info()

─ Session info  🌡️  🤵🏼  🤲🏼   ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
 hash: thermometer, person in tuxedo: medium-light skin tone, palms up together: medium-light skin tone

 setting  value
 version  R version 4.0.5 (2021-03-31)
 os       macOS Catalina 10.15.7
 system   x86_64, darwin17.0
 ui       RStudio
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       America/New_York
 date     2021-12-13
 rstudio  2021.09.0+351 Ghost Orchid (desktop)
 pandoc   2.14.0.3 @ /Applications/RStudio.app/Contents/MacOS/pandoc/ (via rmarkdown)

─ Packages ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
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 rprojroot              2.0.2    2020-11-15 [1] CRAN (R 4.0.2)
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 stringi                1.7.5    2021-10-04 [1] CRAN (R 4.0.2)
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 tidyr                * 1.1.4    2021-09-27 [1] CRAN (R 4.0.2)
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 tidyverse            * 1.3.1    2021-04-15 [1] CRAN (R 4.0.2)
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 viridis                0.6.2    2021-10-13 [1] CRAN (R 4.0.2)
 viridisLite            0.4.0    2021-04-13 [1] CRAN (R 4.0.2)
 vroom                  1.5.5    2021-09-14 [1] CRAN (R 4.0.2)
 withr                  2.4.2    2021-04-18 [1] CRAN (R 4.0.2)
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 XML                    3.99-0.8 2021-09-17 [1] CRAN (R 4.0.2)
 xml2                   1.3.2    2020-04-23 [1] CRAN (R 4.0.2)
 xtable                 1.8-4    2019-04-21 [1] CRAN (R 4.0.2)
 XVector                0.30.0   2020-10-28 [1] Bioconductor
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 [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library

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